Quantitative Researcher

Quantitative Researcher

About Millennium

Millennium is a global, diversified alternative investment firm, founded in 1989. Defined by evolution, innovation and focus, Millennium’s mission is to deliver results for our investors.

Our people are empowered with both independence and support: the autonomy to pursue ideas with conviction and the backing of a global network committed to collaboration, disciplined risk management and continuous learning. With opportunities to deepen expertise and accelerate development, talent at Millennium is equipped to adapt, evolve and build lasting impact over time. Discover how transformative growth accelerates impact.

Meet the Team

The Execution Services team supports trading across products and geographies, with a focus on execution quality, trading efficiency, and the tools that help portfolio managers make informed decisions. Within this group, the Central Liquidity Strategies team is building the models and research platform that power CLS trading, combining quantitative research, live analytics, and close partnership with traders and portfolio managers across global equities markets.

What You'll Do

Design, calibrate, and deliver models across the CLS domain, including market microstructure, factor risk modeling, transaction cost analysis, and alpha signals, with a focus on robust out-of-sample performance across US, EMEA, and APAC equities

Own the full model lifecycle, including performance monitoring, live versus historical reconciliation, drift and decay analysis, recalibration, and retirement decisions

Write, test, support, and maintain production-quality code using strong engineering standards, including unit testing, integration testing, documentation, automation, and CI/CD practices

Partner with team members to shape the direction, design, and architecture of the research platform

Collaborate closely with traders and portfolio managers to understand business needs and validate model behavior in production

What You Bring

6+ years of quantitative research experience in a financial setting, with a strong track record of delivering production models and measurable impact

PhD or Master’s degree in Statistics, Machine Learning, Physics, Mathematics, Computer Science, Financial Engineering, or a related quantitative field

Deep understanding of statistical modeling and machine learning theory, with hands-on experience using methods such as gradient-boosted trees, deep learning with PyTorch, and time-series models

Strong technical skills in Python and kdb+, preferably both, for research and analysis

Experience with Git, Unix/Linux, Bash, and modern CI/CD workflows

Strong communication skills and the ability to explain sophisticated technical concepts clearly and concisely

Creative, hands-on research approach, with the ability to move from idea generation to production-ready models

Experience with equity execution and trading, live analytics, PyKX, distributed training, and/or cloud tooling is a plus